In this document, the topic of discussion is the combination of two existing algorithms to generate a new hybrid technique. The two algorithms that are subjected to said amalgamation are Genetic Algorithms (GA) and Stain Bowerbird Optimization algorithms (SBO). These two methodologies have profound utility themselves and are used in a multitude of scenarios. The easy application and the constructive outcomes manifested by these two algorithms birthed the idea of their combined usage. Following up on this, the hybrid GASBO was created. GASBO was an optimization approach used to detect and categorize the allotted renewable energy assets in a specific energy generation complex. This was done to regulate the energy dispensing systems otherwise known as ‘distributing’ systems. These renewable resources are reflected by environmental factors and the energy they create is also dependent on their surroundings. Factors like sunlight, rain, waves, and tides etcetera play major roles in determining the outcome of the created energy.

Contrary to what it may appear like, the position of the DG sources in the structure affects the outcome a lot. These sources contain fuel cells and photovoltaic cells: in short, devices that can harness energy from a seemingly infinite supply like sunlight. As mentioned before, the GASBO assisted in providing the best location for the system and it also categorized the sources according to their abilities. The potential and position of the sources in the grid are of vast importance. The main purpose of GASBO is to optimize the overall system by improving its efficiency and reducing collateral harm. This shows that GASBO is quite a fundamental tool. It has also been tested on several systems like IEEE 33-bus. The facts in this paper are based on published projects.

在本文档中，讨论的主题是结合两种现有算法以生成新的混合技​​术。经受所述合并的两种算法是遗传算法（GA）和染色鲍尔鸟优化算法（SBO）。这两种方法本身具有深远的实用性，并用于多种场景。这两种算法所表现出的简单应用和建设性结果催生了它们组合使用的想法。在此之后，创建了混合 GASBO。GASBO 是一种优化方法，用于检测和分类特定能源生产综合体中分配的可再生能源资产。这样做是为了调节能量分配系统，也称为“分配”系统。这些可再生资源由环境因素反映，它们产生的能量也取决于它们的周围环境。阳光、雨水、海浪和潮汐等因素在决定所产生能量的结果方面起着重要作用。

与看起来的情况相反，DG 源在结构中的位置对结果影响很大。这些能源包含燃料电池和光伏电池：简而言之，可以从看似无限的供应源（如阳光）中获取能量的设备。如前所述，GASBO 协助为系统提供最佳位置，并根据其能力对来源进行分类。电网中源的潜力和位置非常重要。GASBO 的主要目的是通过提高效率和减少附带损害来优化整个系统。这表明 GASBO 是一个非常基础的工具。它还在多个系统（如 IEEE 33 总线）上进行了测试。本文中的事实基于已发表的项目。